library("gapminder")
library("ggplot2")
library("plotly")
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df.climate <- read.csv(file = 'data/DailyDelhiClimate.csv', header = TRUE, stringsAsFactors = FALSE, strip.white = TRUE, sep = ",")
# convert the character type to class "Date" representing calendar dates.
df.climate$date <- as.Date(df.climate$date, "%m/%d/%Y")
#import data, map variable "date" and "wind_speed" to x and y axis respectively
ggplot(df.climate, aes(x=date, y=wind_speed)) +
#add GEOM_FUNCTION
geom_line()
fig <- plot_ly(df.climate,
x = ~date,
y = ~wind_speed,
type = 'scatter',
mode = 'lines')
fig
#Interactive multiple lines chart for wind speed, mean temperature and humidity
fig <- plot_ly(df.climate,
x = ~date,
y = ~wind_speed,
name = 'Wind speed',
type = 'scatter',
mode = 'lines')
fig <- fig %>% add_trace(y = ~meantemp,
name = 'Mean temperature',
mode = 'lines')
fig <- fig %>% add_trace(y = ~humidity,
name = 'Humidity',
mode = 'lines')
fig
#Customization on the chart. The customization can be done by layout() function.
fig <- plot_ly(df.climate,
x = ~date,
y = ~wind_speed,
name = 'Wind speed',
type = 'scatter',
mode = 'lines+markers')
fig <- fig %>% add_trace(y = ~meantemp,
name = 'Mean temperature',
mode = 'lines+markers')
fig <- fig %>% add_trace(y = ~humidity,
name = 'Humidity',
mode = 'markers')
fig <- fig %>% layout(title = "Daily climate data in the city of Delhi",
xaxis = list(title = "Date",
color = "red"),
yaxis = list(title = ""),
legend = list(title=list(text='<b> Weather data </b>'))
)
fig
# Import the Stroke Prediction Dataset
df.healthcare <- read.csv(file = 'data/healthcare-dataset-stroke-data.csv', header = TRUE, stringsAsFactors = FALSE, strip.white = TRUE, sep = ",")
ggplot(gapminder, aes(x=gdpPercap, y=lifeExp, color=continent)) +
geom_point() +
ggtitle("Relationship between GDP per capita and life expectancy") +
labs(x = "GDP per capita (US$, inflation-adjusted)", y = "Life expectancy at birth, in years") +
scale_x_continuous(breaks = seq(0,113524,10000)) +
scale_y_continuous(breaks = seq(0,100,10))
fig <- plot_ly(df.healthcare, y = ~bmi, color = ~smoking_status, type = "box")
#basic chart
fig
fig <- plot_ly(df.healthcare, y = ~bmi, color = ~smoking_status, type = "box")
#basic chart
fig
fig <- plot_ly(gapminder,
x = ~gdpPercap,
y = ~lifeExp,
size = ~pop,
color = ~continent,
text = ~country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers')
fig
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#Basic animation on the scatter plot by adding the time frame
fig <- plot_ly(gapminder,
x = ~gdpPercap,
y = ~lifeExp,
size = ~pop,
color = ~continent,
frame = ~year, #add the time frame for the animation
text = ~country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers')
fig
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#Customization on the animation
fig <- fig %>% layout(title = "Relationship between GDP per capita and life expectancy",
xaxis = list(title = "GDP per capita (US$, inflation-adjusted)",
type = "log"),
yaxis = list(title = "Life expectancy at birth, in years"))
fig
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# import the modified daily climate time series data.
df.climate <- read.csv(file = 'data/DailyDelhiClimate.csv', header = TRUE, stringsAsFactors = FALSE, strip.white = TRUE, sep = ",")
#select the data in January 2013 only, total 31 days.
df.climate <- df.climate[c(1:31),]
df.climate$date <- seq(1:31)
fig <- plot_ly(df.climate,
x = ~meantemp,
y = ~humidity,
size = ~wind_speed,
frame = ~date,
text = ~meanpressure,
hoverinfo = "text",
type = 'scatter',
mode = 'markers',
showlegend = FALSE)
fig <- fig %>% layout(title = "Relationship between mean temperature, humidity, wind speed and pressure in January 2013",
xaxis = list(title = "Mean temperature"),
yaxis = list(title = "Humidity"))
fig
# Import the Drug Classification Dataset
df.drug <- read.csv(file = 'data/drug.csv', header = TRUE, stringsAsFactors = FALSE, strip.white = TRUE, sep = ",")
#Basic 3D scatter plot
fig <- plot_ly(df.drug,
x = ~ Age,
y = ~Na_to_K,
z = ~BP,
color = ~Drug,
type = 'scatter3d',
mode = 'markers')
fig
#Customization on the 3D scatter plot
fig <- plot_ly(df.drug,
x = ~ Age,
y = ~Na_to_K,
z = ~BP,
color = ~Drug,
type = 'scatter3d',
mode = 'markers')
fig <- fig %>% layout(title = "Relationship between the age, Sodium to Potassium ration and blood pressure levels",
legend = list(title=list(text="<b> Drug type for patient </b>")),
scene = list(xaxis = list(title = 'x-axis: Age of the patient'),
yaxis = list(title = 'y-axis: Sodium (Na) to Potassium (K) ration'),
zaxis = list(title = 'z-axis: Blood pressure levels')))
fig
# Import the dataset "uspopage" about the age distribution of population in the United States, 1900-2002
data(uspopage, package="gcookbook")
fig <- plot_ly(uspopage,
x = ~Year,
y = ~Thousands,
z = ~AgeGroup,
type = 'scatter3d',
mode = 'lines',
color = ~AgeGroup)
fig
#Customization on the 3D line chart
fig <- plot_ly(uspopage,
x = ~Year,
y = ~Thousands,
z = ~AgeGroup,
type = 'scatter3d',
mode = 'lines',
color = ~AgeGroup,
#set the transparency of the line by opacity
opacity = 0.8,
#set the width of the line
line = list(width = 5))
fig <- fig %>% layout(title = "Age distribution of population in the United States, 1900-2002",
scene = list(xaxis = list(title = 'x-axis: Date in year'),
yaxis = list(title = 'y-axis: Number of people, in thousands'),
zaxis = list(title = 'z-axis: Age group of population')))
fig
#Surface plots
x = c(1,2,3,4,5,6)
y = c(1,2,3,4,5)
z = rbind(c(0, 1, 0, 1, 0, 1),
c(1, 0, 1, 0, 1, 0),
c(0, 1, 0, 1, 0, 1),
c(1, 0, 1, 0, 1, 0),
c(0, 1, 0, 1, 0, 1))
#z is a matrix:
z
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 1 0 1 0 1
## [2,] 1 0 1 0 1 0
## [3,] 0 1 0 1 0 1
## [4,] 1 0 1 0 1 0
## [5,] 0 1 0 1 0 1
fig <- plot_ly(x = ~x,
y = ~y,
z = ~z,
type = 'surface')
fig
#Customization on the surface plot
fig <- fig %>% layout(title = "3D surface plot for x, y and z",
scene = list(xaxis = list(title = 'x-axis: 1-6'),
yaxis = list(title = 'y-axis: 1-5'),
zaxis = list(title = 'z-axis: 0-1'),
# axes are drawn in proportion with the input of "aspectratio"
aspectratio = list(x = 1, y = 0.5, z = 0.5)))
fig
#import dataset
data(VADeaths)
#Load plot3D package for 3D histogram
library("plot3D")
hist3D(z = VADeaths)
hist3D(x = 1:5, y = 1:4, z = VADeaths,
phi = 30, theta = -70,
xlab = "", ylab = "", zlab = "", main = "Death Rates in Virginia (1940)",
border = "black", shade = 0.5,
ticktype = "detailed", space = 0.5, d = 2)
# Use text3D to label and position x axis
text3D(x = 1:5, y = rep(0.3, 5), z = rep(5, 5),
labels = rownames(VADeaths),
add = TRUE, adj = -0.5)
# Use text3D to label and position y axis
text3D(x = rep(1, 4), y = 1:4, z = rep(-10, 4),
labels = colnames(VADeaths),
add = TRUE, adj = 1)
# Import the Stroke Prediction Dataset
df.healthcare <- read.csv(file = 'data/healthcare-dataset-stroke-data.csv', header = TRUE, stringsAsFactors = FALSE, strip.white = TRUE, sep = ",")
df.healthcare$hypertension <- as.character(df.healthcare$hypertension)
fig <- plot_ly(df.healthcare,
x = ~ age,
y = ~avg_glucose_level,
z = ~bmi,
color = ~hypertension,
type = 'scatter3d',
mode = 'markers',
size = 0.5)
fig <- fig %>% layout(title = "Relationship between Age, Average glucose level and BMI",
legend = list(title=list(text="<b> 0: no hypertension<br> 1: hypertension </b>")),
scene = list(xaxis = list(title = 'x-axis: Age of the patient'),
yaxis = list(title = 'y-axis: Average glucose level in blood (mg/dL)'),
zaxis = list(title = 'z-axis: Body mass index')))
fig
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels